---
title: Agentic RAG with Reranking
---

## Code

```python
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reranker import CohereReranker
from agno.models.openai import OpenAIChat
from agno.vectordb.lancedb import LanceDb, SearchType

knowledge = Knowledge(
    vector_db=LanceDb(
        uri="tmp/lancedb",
        table_name="agno_docs",
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(
            id="text-embedding-3-small"
        ),
        reranker=CohereReranker(
            model="rerank-multilingual-v3.0"
        ),
    ),
)

knowledge.add_content(
    name="Agno Docs", url="https://docs.agno.com/introduction.md"
)

agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    knowledge=knowledge,
    markdown=True,
)

if __name__ == "__main__":
    # Load the knowledge base, comment after first run
    # agent.knowledge.load(recreate=True)
    agent.print_response("What are Agno's key features?")

```

## Usage

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Set your API keys">
    ```bash
    export OPENAI_API_KEY=xxx
    export COHERE_API_KEY=xxx
    ```
  </Step>

  <Step title="Install libraries">
    ```bash
    pip install -U openai lancedb tantivy pypdf sqlalchemy agno cohere
    ```
  </Step>

  <Step title="Run Agent">
    <CodeGroup>
    ```bash Mac
    python cookbook/agents/rag/agentic_rag_with_reranking.py
    ```

    ```bash Windows
    python cookbook/agents/rag/agentic_rag_with_reranking.py
    ```
    </CodeGroup>
  </Step>
</Steps>